Think Only When You Need with Large Hybrid-Reasoning Models

Adaptive Hybrid-Reasoning in Large Language Models for Efficient Problem Solving

Published

May 20, 2025

Authors: L. Jiang et al. 

Published on Arxiv: 2025-05-20

Link: http://arxiv.org/abs/2505.14631v1

Institutions: Microsoft Research • Peking University

Keywords: Large Language Models, Large Reasoning Models, Hybrid Reasoning, Reinforcement Learning from Human Feedback, Efficient AI, Policy Optimization, System 1 and System 2 reasoning, Adaptive Reasoning, Qwen-2.5, DeepSeek-R1, Hybrid Fine-Tuning, Hybrid Group Policy Optimization, Hybrid Accuracy, Mathematical Reasoning, Code Generation, General Problem Solving

Recent advancements in Large Reasoning Models (LRMs) have greatly improved reasoning abilities over standard Large Language Models (LLMs), particularly for mathematics, programming, and complex problem-solving. However, excessive or unnecessary reasoning—especially on simple queries—introduces notable computational and latency costs, highlighting the need for more efficient and context-adaptive AI systems.

To address these efficiency challenges, the authors propose an adaptive hybrid-reasoning framework with several key contributions:

The experimental results clearly demonstrate the advantages of this approach:

In summary, the work paves the way for more efficient, human-aligned AI assistants by innovatively balancing direct answering and complex reasoning: